code: def h2o_classification_pipeline_evaluation():
      data = get_iris_data()
      pipeline = pipeline_h2o_class()
      train_data, test_data = train_test_data_setup(data, shuffle=True)
  
      pipeline.fit(input_data=train_data)
      results = pipeline.predict(
          input_data=test_data, output_mode="full_probs"
      )
      prediction_before_export = results.predict[:, 0]
      print(f"Before export {prediction_before_export[:4]}")
      roc_auc_on_test = roc_auc(
          y_true=test_data.target,
          y_score=results.predict,
          multi_class="ovo",
          average="macro",
      )
      #  H2o has troubles with serialization for now
      #  pipeline_path = "h2o_class"1
      #  export_h2o(pipeline, pipeline_path, test_data)
      print(f"roc auc: {roc_auc_on_test}")
  
file_path: FEDOT/examples/advanced/automl/h2o_example.py
imports: import numpy
  from sklearn.metrics import roc_auc_score, mean_squared_error, mean_absolute_error
  from examples.advanced.time_series_forecasting.composing_pipelines import visualise
  from examples.simple.pipeline_import_export import create_correct_path
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from test.unit.tasks.test_classification import get_iris_data
  from test.unit.tasks.test_forecasting import get_ts_data
  from test.unit.tasks.test_regression import get_synthetic_regression_data, get_rmse_value
label_desc: Use image node_id 5 for calling a global function with example usage: h2o_classification_pipeline_evaluation() without return types